← Back to home
Comparison · Analytics

dfms vs taxizedb

A side-by-side editorial comparison of dfms and taxizedb — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:ropensci

dfms vs taxizedb: at a glance

Featuredfmstaxizedb
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropenscitaxonomy, biodiversity-data, sqlite, ropensci
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is dfms?

Peer-reviewed, feature-complete, and now able to hand its models to other forecasting engines.

dfms estimates dynamic factor models in R, the workhorse for nowcasting economic activity from ragged, mixed-frequency data. The package worked through the Banbura and Modugno (2014) specification in stages — quarterly variables in 0.3.0, AR(1) idiosyncratic errors combined with mixed frequency in 0.4.0 — then declared 1.0.0 feature-complete on completing rOpenSci peer review, adding news decomposition to attribute forecast revisions to specific data releases. Version 1.0.1 adds convert(), which exports fitted models to dlm or KFAS state-space objects.

Read the full dfms trajectory →

What is taxizedb?

Stopped trusting the cloud to prepare its taxonomic databases and does the conversion locally.

taxizedb queries taxonomic databases locally rather than through rate-limited web APIs, which is what makes it usable for millions of name lookups. Version 0.2.0 established the current design: every source stored as SQLite, no credentials or ports needed, plus name-to-ID mapping functions and ports of the core taxize verbs. The most recent release changes how those databases arrive — instead of downloading a preprocessed SQLite file from the cloud, db_download_*() now fetches raw data and converts it locally for every source, because the cloud path kept breaking.

Read the full taxizedb trajectory →

dfms vs taxizedb: editorial side-by-side

D
dfms
ANALYTICS
0.0

Peer-reviewed, feature-complete, and now able to hand its models to other forecasting engines.

◆ Current state

dfms estimates dynamic factor models in R, the workhorse for nowcasting economic activity from ragged, mixed-frequency data. The package worked through the Banbura and Modugno (2014) specification in stages — quarterly variables in 0.3.0, AR(1) idiosyncratic errors combined with mixed frequency in 0.4.0 — then declared 1.0.0 feature-complete on completing rOpenSci peer review, adding news decomposition to attribute forecast revisions to specific data releases. Version 1.0.1 adds convert(), which exports fitted models to dlm or KFAS state-space objects.

◆ Where it's heading

The package has finished the implementation programme it set out in its 2023 vignette and is now working on the edges: interoperability with other state-space packages rather than more estimation methods of its own. The convert() function is the clearest signal — instead of implementing smoothing and prediction intervals natively, it hands the model to packages that already have them. The rOpenSci move also puts it on a review-backed, documented footing that research users can cite.

◆ Prediction

Expect continued interoperability and diagnostic work rather than new estimators, since the maintainer has explicitly scoped the package as complete. Bug fixes against RcppArmadillo releases will likely remain the other recurring driver.

T
taxizedb
ANALYTICS
0.0

Stopped trusting the cloud to prepare its taxonomic databases and does the conversion locally.

◆ Current state

taxizedb queries taxonomic databases locally rather than through rate-limited web APIs, which is what makes it usable for millions of name lookups. Version 0.2.0 established the current design: every source stored as SQLite, no credentials or ports needed, plus name-to-ID mapping functions and ports of the core taxize verbs. The most recent release changes how those databases arrive — instead of downloading a preprocessed SQLite file from the cloud, db_download_*() now fetches raw data and converts it locally for every source, because the cloud path kept breaking.

◆ Where it's heading

The package is trading convenience for independence. Each release removes another thing that has to be working elsewhere for the package to function: hosted database preparation is gone, and where a provider disappears the package documents it rather than pretending otherwise — db_download_tpl() is now defunct because The Plant List no longer exists, though previously downloaded copies still query fine. Release cadence is slow, with multi-year gaps and a maintainer handover in 2023.

◆ Prediction

Expect further releases to track data sources appearing and disappearing rather than adding features, since that has driven every recent change. Local conversion also shifts cost onto users, so build time and memory for the larger sources are the plausible next thing to need attention.

Alternatives to dfms and taxizedb

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either dfms or taxizedb.

See all dfms alternatives → · See all taxizedb alternatives →

Recent activity from dfms and taxizedb

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
  2. 6mo agodfms1.0: rOpenSci review passed, news decomposition added
  3. 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
  4. 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
  5. 9mo agotaxizedbDatabases now built locally from raw data, not the cloud
  6. 1y agodfmsFixes estimation with a single quarterly variable
  7. 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
  8. 3y agotaxizedbPatch release for a maintainer change
  9. 5y agotaxizedbtaxa_at() retrieves ancestors at a named rank
  10. 5y agotaxizedbFixes failing tests
  11. 6y agotaxizedbSQLite everywhere, three new sources, taxize verbs ported
  12. 9y agotaxizedbTracks the dplyr split that introduced dbplyr

Frequently asked questions

What is the difference between dfms and taxizedb?

Both compete on the same themes — ropensci — within Analytics. dfms and taxizedb are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is dfms better than taxizedb?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dfms and taxizedb are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to dfms?

Top dfms alternatives in Analytics are ranked by recent ship velocity. Browse the "dfms alternatives" section above for the current picks, or visit /alternatives/dfms for the full list with editorial commentary on each.

What are the best alternatives to taxizedb?

Top taxizedb alternatives in Analytics are ranked by recent ship velocity. Browse the "taxizedb alternatives" section above for the current picks, or visit /alternatives/taxizedb for the full list with editorial commentary on each.